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Batteries are interconnected in series and/or parallel to meet wide-range power or energy demands in various industrial applications. To pursue the benefits of multiple connection structures in one system, reconfigurable battery systems (RBSs) have recently emerged for safe and efficient operation, extended energy storage, delivery, etc. Switches are the essential elements to enable the battery system reconfiguration, but selecting appropriate switches for RBS designs has not been systematically investigated. To bridge this gap, analytical expressions are derived in this article to estimate the maximum switch current and its upper limit to facilitate the selection of RBS switches. An RBS prototype based on H-bridges is set up and experimental results verify the effectiveness and advantage of the proposed estimation method. These analytical expressions, relying only on resistances of batteries and switches, are readily applicable to practical RBS design and much more efficient than conducting numerous circuit experiments, simulation tests, or circuit analyses, especially for large-scale systems. Moreover, the analysis framework and estimation method proposed for series–parallel mutual conversion can be adaptively extended to other complex system reconfigurations to facilitate various RBS designs. [ABSTRACT FROM AUTHOR]

Knowing the health state of the batteries would enhance the energy storage system’s reliability and safety, especially for fast charge applications. Here we propose a synergetic method with the help of the genetic algorithm (GA) and the support vector regression (SVR) for SOH estimation. Firstly, features for battery aging process description are selected from the multi-source data, including current, voltage, and temperature, in the battery charging process. The SVR is then employed to establish a battery aging model and estimate the SOH with the generated features. Afterward, the feature set which can optimize the pre-set objective, namely minimizing the SOH estimation error and the defined difficulty of feature acquisition, are selected by the GA via an iterative process. Experimental results indicate that the selected feature set generated from the charged capacity and temperature rise data may perform a better SOH estimation than the manually picked features and the optimized ones from a single source. Moreover, by collaborating with the chosen features, the SVR is found to have a similar SOH estimation accuracy to a more complex algorithm while using less computation power. Furthermore, it should be noted that the selected features are obtainable in about 95% of the charging operations according to the voltage distribution resulting from more than 40,000 actual charging bills. [ABSTRACT FROM AUTHOR]

Battery prognostics and health management (PHM) is essential for lithium-ion batteries in electric vehicles. In the battery PHM, accurate estimation of the battery state-of-health (SOH) and prediction of the remaining useful life (RUL) are crucial to ensure a safe and efficient battery operation. This article presents a probabilistic method for the battery degradation modeling and health prognosis based on the features extracted from the charging process using the dynamic Bayesian network (DBN). First, an aggregated feature, combining the incremental capacity analysis of constant-current (CC) charging and the time constant of constant-voltage (CV) charging, is developed to characterize the battery degradation dynamics in case some CC or CV charging information is absent. The DBN is then employed to explore the underlying correlation between the battery health indicator and the extracted features. The proposed model treats the degradation dynamics as a rich family of probability distributions to model real-world battery operation more accurately. Moreover, the battery SOH estimation and RUL prediction are carried out using the particle filtering inference algorithm. Experimental tests are conducted on two different battery cells and the results show that the proposed methods can provide an accurate and robust battery SOH estimation and reliable RUL prediction. [ABSTRACT FROM AUTHOR]

The heterogeneity of cells in a battery pack is inevitable but brings high risks of premature failure and even safety hazards. Accordingly, for safe and long-life operation, it is necessary to adjust the state of charge (SOC) of all in-pack cells to the same level. To address this problem, this article first proposes a battery SOC observer and analyzes its stability and convergence analysis using the Lyapunov direct method. Different to most available estimators is that the proposed method does not require the information of cell capacities. Then, after modeling the equalization system as a directed graph, the equalization problem is cast as a path searching problem. Finally, an A-star algorithm subject to balancing constraints is proposed to find the shortest path in this graph, corresponding to the most efficient SOC equalization. Experimental results show that the steady-state error of the proposed observer is less than 2%. It also demonstrates that the A-star algorithm can decrease the balancing time and energy loss during the balancing process by 9.59% and 19.5%, respectively, relative to the mean-difference-average method. [ABSTRACT FROM AUTHOR]

The state of charge (SOC) of lithium-ion batteries (LIBs) is an important evaluation index in battery management system of energy storage systems. However, it is always a challenging task to accurately estimate SOC of LIBs, because of the existence of nonlinear characteristics and significant temperature effects. To improve the SOC estimation accuracy and robustness, a model-based estimation approach for SOC and impedance of LIBs is proposed against uncertain loading profiles and ambient temperatures. First an auto regressive exogenous model for online model order determining and parameters identification is established to monitor parameters variations based on numerical subspace state space system identification method. Second, a sequential Monte Carlo filter is employed to overcome the nonlinear and non-Gaussian error distribution state estimation problems caused by complicated open-circuit voltage characteristics. Finally, evaluation of the adaptability and generality of the proposed method are verified by different LIBs under different operating conditions. Experimental results indicate that the proposed method shows great performance, whose estimation value converges to real SOC within an error of $\pm \text{3}\%$ , and the battery model can simulate battery dynamics robustly with high accuracy. [ABSTRACT FROM AUTHOR]

Accurate estimation of power capacity is critical to ensure battery safety margins and optimize energy utilization. Power capacity estimators based on online identified equivalent circuit model have been widely investigated due to the high accuracy and affordable computing cost. However, the impact of noise corruption which is common in practice on such estimators has never been investigated. This paper scrutinizes the effect of noises on model identification, state of charge (SOC) and power capacity estimation. An online model identification method based on adaptive forgetting recursive total least squares (AF-RTLS) is proposed to compensate the noise effect and attenuate the identification bias of model parameters. A Luenberger observer is further used in combination with the AF-RTLS to estimate the SOC in real time. Leveraging the estimated model parameters and SOC, a multiconstraint analytical method is proposed to online estimate the power capacity. Simulation and experimental results verify that the proposed method is superior in terms of estimation accuracy and the robustness to noise corruption. [ABSTRACT FROM AUTHOR]

Accurate remaining useful life (RUL) prediction and state-of-health (SOH) diagnosis are of extreme importance for safety, durability, and cost of energy storage systems based on lithium-ion batteries. It is also a crucial challenge for energy storage systems to predict RUL and diagnose SOH of batteries due to the complicated aging mechanism. In this paper, a novel method for battery RUL prediction and SOH estimation is proposed. First, a novel support vector regression-based battery SOH state-space model is established to simulate the battery aging mechanism, which takes the capacity as the state variable and takes the representative features during a constant-current and constant-voltage protocol as the input variables. The estimated impedance variables are taken as the output due to the correlation between battery capacity and the sum of charge transfer resistance and electrolyte resistance. Second, in order to suppress the measurement noises of current and voltage, a particle filter is employed to estimate the impedance degradation parameters. Furthermore, experiments are conducted to validate the proposed method. The results show that the proposed SOH estimation method can provide an accurate and robustness result. The proposed RUL prediction framework can also ensure an accurate RUL prediction result. [ABSTRACT FROM PUBLISHER]